How To Assure Quality Of Big Data and Analytics Solutions?

With the widespread applications of Big Data and Analytics Solutions, it is really crucial to know their success factor. Big data and Data Analytics are not only used in the organizations but they are used in many practical cases also. How To Assure Quality of Big Data and Analytics Solutions is the main challenge here.

In Big Data assurance space, the recommendation, prediction and the decision are increasing in large popularity. But in order to make it run smoothly, there are many quality problems found. This quality a problem leads to unwanted costs to the companies. In one of the research carried out by Chaunqi Tao and Jerry Gao found that there are many quality parameters. The assurance methods, criteria, standards all needs to follow the quality parameters.

According to the Researchers, Chaunqi and Jerry highlighted the four necessary processes.

The studying of functions like the algorithms, oracles, domain specific methods and learning capabilities are important in Big Data System.

Functions like security, quality of service, robustness and system consistency are the Big Data systems main functions.

Lifetime testing, continuous testing, real-time testing and testing few other real time features.

Features like system evolution, usability, visualization are few of the main key factors also.

This features and the researches helps in determining the good elements. Some of them are:

System data security- This helps in identifying the data security part. Data security is really looked after by the people.

Performance of the system- Response, time, security and availability are determined.

Robustness- It is really crucial to identify how the system adapts changes, in the initial changes. When anything gets changed it needs to be studied how stable the system can be.

Reliability- A system can be considered reliable only when the system behaves the way it is designed. After testing, how the system is functioning should be observed.

It is found out by Experian’s that 14 percent of the revenue the business are wasting due to the low quality of data produced. It is thus absolutely mandatory to test and analyze the data first before using it or applying it.

Rajni Sachan of TCS, highlighted How to Assure Quality of Big Data and Analytics Solutions. There are 4 pillars for this.

People- The right set of people assigned for the right task in the end give the right result. So any team can work smoothly.

Process- Analyzing the loop holes of the data. It shows that data should be analyzed first then they should be delivered. The thorough analysis can reduce the cost in the longer run.

Automation-The right tools and scripts can increase the productivity and reduce the chances of faults. A mistake mostly occurs due to manual interventions.

Infrastructure- To minimize investment on the first go, scalable cloud infrastructure is required to be studied.

So to avoid mistakes, high costs it is necessary to Assure Quality of Big Data and Analytics Solutions. The business can save their revenue just by following the simple steps.